Clarification of submitted questions in a question and answer system

ABSTRACT

Mechanisms for clarifying an input question are provided. A question is received for generation of an answer. A set of candidate answers is generated based on an analysis of a corpus of information. Each candidate answer has an evidence passage supporting the candidate answer. Based on the set of candidate answers, a determination is made as to whether clarification of the question is required. In response to a determination that clarification of the question is required, a request is sent for user input to clarify the question. User input is received from the computing device in response to the request and at least one candidate answer in the set of candidate answers is selected as an answer for the question based on the user input.

BACKGROUND

The present application relates generally to an improved data processingapparatus and method and more specifically to mechanisms for identifyingunique criteria for clarifying a submitted question in a question andanswer (QA) system.

With the increased usage of computing networks, such as the Internet,humans are currently inundated and overwhelmed with the amount ofinformation available to them from various structured and unstructuredsources. However, information gaps abound as users try to piece togetherwhat they can find that they believe to be relevant during searches forinformation on various subjects. To assist with such searches, recentresearch has been directed to generating Question and Answer (QA)systems which may take an input question, analyze it, and return resultsindicative of the most probable answer to the input question. QA systemsprovide automated mechanisms for searching through large sets of sourcesof content, e.g., electronic documents, and analyze them with regard toan input question to determine an answer to the question and aconfidence measure as to how accurate an answer is for answering theinput question.

One such QA system is the Watson™ system available from InternationalBusiness Machines (IBM) Corporation of Armonk, N.Y. The Watson™ systemis an application of advanced natural language processing, informationretrieval, knowledge representation and reasoning, and machine learningtechnologies to the field of open domain question answering. The Watson™system is built on IBM's DeepQA™ technology used for hypothesisgeneration, massive evidence gathering, analysis, and scoring. DeepQA™takes an input question, analyzes it, decomposes the question intoconstituent parts, generates one or more hypothesis based on thedecomposed question and results of a primary search of answer sources,performs hypothesis and evidence scoring based on a retrieval ofevidence from evidence sources, performs synthesis of the one or morehypothesis, and based on trained models, performs a final merging andranking to output an answer to the input question along with aconfidence measure.

Various United States Patent Application Publications describe varioustypes of question and answer systems. U.S. Patent ApplicationPublication No. 2011/0125734 discloses a mechanism for generatingquestion and answer pairs based on a corpus of data. The system startswith a set of questions and then analyzes the set of content to extractanswer to those questions. U.S. Patent Application Publication No.2011/0066587 discloses a mechanism for converting a report of analyzedinformation into a collection of questions and determining whetheranswers for the collection of questions are answered or refuted from theinformation set. The results data are incorporated into an updatedinformation model.

SUMMARY

In one illustrative embodiment, a method, in a data processing systemcomprising a processor and a memory, for clarifying an input question.The method comprises receiving, in the data processing system from acomputing device, the input question for generation of an answer to theinput question. The method further comprises generating, in the dataprocessing system, a set of candidate answers for the input questionbased on an analysis of a corpus of information, wherein each candidateanswer in the set of candidate answers has an evidence passagesupporting the candidate answer as answering the input question.Moreover, the method comprises determining, in the data processingsystem, based on the set of candidate answers, whether clarification ofthe input question is required and sending, by the data processingsystem, in response to a determination that clarification of the inputquestion is required, a request for user input to clarify the inputquestion. In addition, the method comprises receiving, in the dataprocessing system, user input from the computing device in response tothe request and selecting, by the data processing system, at least onecandidate answer in the set of candidate answers as an answer for theinput question based on the user input.

In other illustrative embodiments, a computer program product comprisinga computer useable or readable medium having a computer readable programis provided. The computer readable program, when executed on a computingdevice, causes the computing device to perform various ones of, andcombinations of, the operations outlined above with regard to the methodillustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided.The system/apparatus may comprise one or more processors and a memorycoupled to the one or more processors. The memory may compriseinstructions which, when executed by the one or more processors, causethe one or more processors to perform various ones of, and combinationsof, the operations outlined above with regard to the method illustrativeembodiment.

These and other features and advantages of the present invention will bedescribed in, or will become apparent to those of ordinary skill in theart in view of, the following detailed description of the exampleembodiments of the present invention.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectivesand advantages thereof, will best be understood by reference to thefollowing detailed description of illustrative embodiments when read inconjunction with the accompanying drawings, wherein:

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion and answer (QA) system in a computer network;

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments may be implemented;

FIG. 3 illustrates a QA system pipeline for processing an input questionin accordance with one illustrative embodiment;

FIGS. 4A-4C illustrate examples of user interfaces and clarificationquestions that may be generated in accordance with illustrativeembodiments of the present invention; and

FIG. 5 is a flowchart outlining an example operation for clarifying theimplied context of an input question in accordance with one illustrativeembodiment.

DETAILED DESCRIPTION

The illustrative embodiments provide mechanisms for identifying uniquecriteria for clarifying a submitted question in a question and answer(QA) system. That is, in a QA system, a user submits a question in anatural language form, i.e. unstructured form, to the QA system whichsearches a corpus of information to identify one or more candidateanswers to the submitted question from evidence passages in the corpusof information. However, users often submit questions with multiple“correct” answers based on the context given in the submitted question.In other words, a user submitted question often contains implied contextthat is intuitive to humans but is not intuitive to algorithmic QAsystems.

For example, consider the question “Who was the first president?” Thereare many “first presidents” of different nations, corporations, or anyother organization. The first president to die in office, as well asmany other first presidents for various other contexts, may also be avalid answer to this submitted question as well since the QA systemcannot determine from the question itself the implied context.

Assume that the QA system has the following two evidence passages thatare analyzed as part of the search of the corpus of information based onthe analysis of the submitted question:

-   -   “The historian Bartolome Mitre stated that Manuel Belgrano held        a deep admiration for George Washington, leader of the American        Revolution and first President of the United States. Because of        it, he worked in the translation of George Washington's Farewell        Address into the Spanish language.”    -   “William Bullein Johnson (A.M. 1814)—South Carolina Baptist        leader and first president of the Southern Baptist Convention.        Associate of first president of Columbian College (later The        George Washington University) William Staughton and Luther        Rice.”        From these evidence passages, the QA system may form two        hypotheses that “George Washington” and “William Johnson” are        candidate answers for the submitted question. However, the        question cannot be answered with confidence without additional        context in the submitted question to specify the president of        what. Known QA systems do not have a viable mechanism for        clarifying unstructured questions with multiple candidate        answers.

The illustrative embodiments provide mechanisms for disambiguating usersubmitted questions in a QA system which have multiple “correct”candidate answers, i.e. candidate answers having a threshold level ofconfidence indicative of the candidate answer being potentially correctfor the submitted question. With the mechanisms of the illustrativeembodiments, the QA system is augmented to include logic for determiningdifferentiating facts, concepts, or semantic relationships (referred toherein collectively as “differentiating factors”) in the evidencepassages leading to the identification of potentially “correct”candidate answers. The logic of the QA system then interactivelycommunicates with the user that submitted the originally submittedquestion based on the identified differentiating facts, concepts, orsemantic relationships to further identify the implied context of theoriginally submitted question. In so doing, the user interactivelyclarifies their originally submitted question to thereby enable the QAsystem to identify which of the potentially “correct” candidate answersis considered to be the most likely correct answer for the originallysubmitted question.

Based on the user's input to the interactive communications forclarifying the implied context of the originally submitted question,weights associated with confidence scores, or components of confidencescores, may be adjusted to increase/decrease these confidence scores orcomponents to increase or improve the scores associated with the impliedcontext of the originally submitted question and reduce or lessen thescores that are not associated with the implied context of theoriginally submitted question. In some cases, candidate answers as awhole may be eliminated from consideration if their evidence passagesindicate that the evidence is for a different context from that of theimplied context of the originally submitted question as clarifiedthrough the interactive communications with the user.

Once the implied context of the input question is further clarifiedthrough the interactive communications, and the candidate answers aremodified based on the user input clarifying the implied context, the QAsystem may generate a final ranked listing of candidate answers and/or afinal answer for the originally submitted question. The ranked listingand/or final answer may be returned to the submitter of the originallysubmitted question. Confidence scores, evidence passage information, andthe like, may be returned with the ranked listing and/or final answer aswell.

The above aspects and advantages of the illustrative embodiments of thepresent invention will be described in greater detail hereafter withreference to the accompanying figures. It should be appreciated that thefigures are only intended to be illustrative of exemplary embodiments ofthe present invention. The present invention may encompass aspects,embodiments, and modifications to the depicted exemplary embodiments notexplicitly shown in the figures but would be readily apparent to thoseof ordinary skill in the art in view of the present description of theillustrative embodiments.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method, or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in any one or more computer readablemedium(s) having computer usable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be a system, apparatus, or device of an electronic,magnetic, optical, electromagnetic, or semiconductor nature, anysuitable combination of the foregoing, or equivalents thereof. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical device havinga storage capability, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiberbased device, a portable compact disc read-only memory (CDROM), anoptical storage device, a magnetic storage device, or any suitablecombination of the foregoing. In the context of this document, acomputer readable storage medium may be any tangible medium that cancontain or store a program for use by, or in connection with, aninstruction execution system, apparatus, or device.

In some illustrative embodiments, the computer readable medium is anon-transitory computer readable medium. A non-transitory computerreadable medium is any medium that is not a disembodied signal orpropagation wave, i.e. pure signal or propagation wave per se. Anon-transitory computer readable medium may utilize signals andpropagation waves, but is not the signal or propagation wave itself.Thus, for example, various forms of memory devices, and other types ofsystems, devices, or apparatus, that utilize signals in any way, suchas, for example, to maintain their state, may be considered to benon-transitory computer readable media within the scope of the presentdescription.

A computer readable signal medium, on the other hand, may include apropagated data signal with computer readable program code embodiedtherein, for example, in a baseband or as part of a carrier wave. Such apropagated signal may take any of a variety of forms, including, but notlimited to, electro-magnetic, optical, or any suitable combinationthereof. A computer readable signal medium may be any computer readablemedium that is not a computer readable storage medium and that cancommunicate, propagate, or transport a program for use by or inconnection with an instruction execution system, apparatus, or device.Similarly, a computer readable storage medium is any computer readablemedium that is not a computer readable signal medium.

Computer code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, radio frequency (RF), etc., or anysuitable combination thereof.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java™, Smalltalk™, C++, or the like, and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer, or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to the illustrativeembodiments of the invention. It will be understood that each block ofthe flowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions thatimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus, or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

Thus, the illustrative embodiments may be utilized in many differenttypes of data processing environments. FIGS. 1-3 are directed todescribing an example Question/Answer, Question and Answer, or QuestionAnswering (QA) system, methodology, and computer program product withwhich the mechanisms of the illustrative embodiments may be implemented.As will be discussed in greater detail hereafter, the illustrativeembodiments may be integrated in, and may augment and extend thefunctionality of, these QA mechanisms with regard to clarifying thecontext of submitted questions through an interactive communication witha user so as to increase the accuracy of the answering of submittedquestions where the context of the submitted question is ambiguous.Thus, it is important to first have an understanding of how question andanswer creation in a QA system may be implemented before describing howthe mechanisms of the illustrative embodiments are integrated in andaugment such QA systems. It should be appreciated that the QA mechanismsdescribed in FIGS. 1-3 are only examples and are not intended to stateor imply any limitation with regard to the type of QA mechanisms withwhich the illustrative embodiments may be implemented. Manymodifications to the example QA system shown in FIGS. 1-3 may beimplemented in various embodiments of the present invention withoutdeparting from the spirit and scope of the present invention.

QA mechanisms operate by accessing information from a corpus of data orinformation (also referred to as a corpus of content), analyzing it, andthen generating answer results based on the analysis of this data.Accessing information from a corpus of data typically includes: adatabase query that answers questions about what is in a collection ofstructured records, and a search that delivers a collection of documentlinks in response to a query against a collection of unstructured data(text, markup language, etc.). Conventional question answering systemsare capable of generating answers based on the corpus of data and theinput question, verifying answers to a collection of questions for thecorpus of data, correcting errors in digital text using a corpus ofdata, and selecting answers to questions from a pool of potentialanswers, i.e. candidate answers.

Content creators, such as article authors, electronic document creators,web page authors, document database creators, and the like, maydetermine use cases for products, solutions, and services described insuch content before writing their content. Consequently, the contentcreators may know what questions the content is intended to answer in aparticular topic addressed by the content. Categorizing the questions,such as in terms of roles, type of information, tasks, or the like,associated with the question, in each document of a corpus of data mayallow the QA system to more quickly and efficiently identify documentscontaining content related to a specific query. The content may alsoanswer other questions that the content creator did not contemplate thatmay be useful to content users. The questions and answers may beverified by the content creator to be contained in the content for agiven document. These capabilities contribute to improved accuracy,system performance, machine learning, and confidence of the QA system.Content creators, automated tools, or the like, may annotate orotherwise generate metadata for providing information useable by the QAsystem to identify these question and answer attributes of the content.

Operating on such content, the QA system generates answers for inputquestions using a plurality of intensive analysis mechanisms whichevaluate the content to identify the most probable answers, i.e.candidate answers, for the input question. The illustrative embodimentsleverage the work already done by the QA system to reduce thecomputation time and resource cost for subsequent processing ofquestions that are similar to questions already processed by the QAsystem.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion/answer creation (QA) system 100 in a computer network 102. Oneexample of a question/answer generation which may be used in conjunctionwith the principles described herein is described in U.S. PatentApplication Publication No. 2011/0125734, which is herein incorporatedby reference in its entirety. The QA system 100 may be implemented onone or morecomputing devices 104 (comprising one or more processors andone or more memories, and potentially any other computing deviceelements generally known in the art including buses, storage devices,communication interfaces, and the like) connected to the computernetwork 102. The network 102 may include multiple computing devices 104in communication with each other and with other devices or componentsvia one or more wired and/or wireless data communication links, whereeach communication link may comprise one or more of wires, routers,switches, transmitters, receivers, or the like. The QA system 100 andnetwork 102 may enable question/answer (QA) generation functionality forone or more QA system users via their respective computing devices110-112. Other embodiments of the QA system 100 may be used withcomponents, systems, sub-systems, and/or devices other than those thatare depicted herein.

The QA system 100 may be configured to implement a QA system pipeline108 that receive inputs from various sources. For example, the QA system100 may receive input from the network 102, a corpus of electronicdocuments 106, QA system users, or other data and other possible sourcesof input. In one embodiment, some or all of the inputs to the QA system100 may be routed through the network 102. The various computing devices104 on the network 102 may include access points for content creatorsand QA system users. Some of the computing devices 104 may includedevices for a database storing the corpus of data 106 (which is shown asa separate entity in FIG. 1 for illustrative purposes only). Portions ofthe corpus of data 106 may also be provided on one or more other networkattached storage devices, in one or more databases, or other computingdevices not explicitly shown in FIG. 1. The network 102 may includelocal network connections and remote connections in various embodiments,such that the QA system 100 may operate in environments of any size,including local and global, e.g., the Internet.

In one embodiment, the content creator creates content in a document ofthe corpus of data 106 for use as part of a corpus of data with the QAsystem 100. The document may include any file, text, article, or sourceof data for use in the QA system 100. QA system users may access the QAsystem 100 via a network connection or an Internet connection to thenetwork 102, and may input questions to the QA system 100 that may beanswered by the content in the corpus of data 106. In one embodiment,the questions may be formed using natural language. The QA system 100may interpret the question and provide a response to the QA system user,e.g., QA system user 110, containing one or more answers to thequestion. In some embodiments, the QA system 100 may provide a responseto users in a ranked list of candidate answers.

The QA system 100 implements a QA system pipeline 108 which comprises aplurality of stages for processing an input question, the corpus of data106, and generating answers for the input question based on theprocessing of the corpus of data 106. The QA system pipeline 108 will bedescribed in greater detail hereafter with regard to FIG. 3.

In some illustrative embodiments, the QA system 100 may be the Watson™QA system available from International Business Machines Corporation ofArmonk, N.Y., which is augmented with the mechanisms of the illustrativeembodiments described hereafter. The Watson™ QA system may receive aninput question which it then parses to extract the major features of thequestion, that in turn are then used to formulate queries that areapplied to the corpus of data. Based on the application of the queriesto the corpus of data, a set of hypotheses, or candidate answers to theinput question, are generated by looking across the corpus of data forportions of the corpus of data that have some potential for containing avaluable response to the input question.

The Watson™ QA system then performs deep analysis on the language of theinput question and the language used in each of the portions of thecorpus of data found during the application of the queries using avariety of reasoning algorithms. There may be hundreds or even thousandsof reasoning algorithms applied, each of which performs differentanalysis, e.g., comparisons, and generates a score. For example, somereasoning algorithms may look at the matching of terms and synonymswithin the language of the input question and the found portions of thecorpus of data. Other reasoning algorithms may look at temporal orspatial features in the language, while others may evaluate the sourceof the portion of the corpus of data and evaluate its veracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the Watson™ QA system. Thestatistical model may then be used to summarize a level of confidencethat the Watson™ QA system has regarding the evidence that the potentialresponse, i.e. candidate answer, is inferred by the question. Thisprocess may be repeated for each of the candidate answers until theWatson™ QA system identifies candidate answers that surface as beingsignificantly stronger than others and thus, generates a final answer,or ranked set of answers, for the input question. More information aboutthe Watson™ QA system may be obtained, for example, from the IBMCorporation website, IBM Redbooks, and the like. For example,information about the Watson™ QA system can be found in Yuan et al.,“Watson and Healthcare,” IBM developerWorks, 2011 and “The Era ofCognitive Systems: An Inside Look at IBM Watson and How it Works” by RobHigh, IBM Redbooks, 2012.

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments may be implemented. Dataprocessing system 200 is an example of a computer, such as server 104 orclient 110 in FIG. 1, in which computer usable code or instructionsimplementing the processes for illustrative embodiments of the presentinvention may be located. In one illustrative embodiment, FIG. 2represents a server computing device, such as a server 104, which, whichimplements a QA system 100 and QA system pipeline 108 augmented toinclude the additional mechanisms of the illustrative embodimentsdescribed hereafter.

In the depicted example, data processing system 200 employs a hubarchitecture including north bridge and memory controller hub (NB/MCH)202 and south bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 areconnected to NB/MCH 202. Graphics processor 210 may be connected toNB/MCH 202 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 212 connectsto SB/ICH 204. Audio adapter 216, keyboard and mouse adapter 220, modem222, read only memory (ROM) 224, hard disk drive (HDD) 226, CD-ROM drive230, universal serial bus (USB) ports and other communication ports 232,and PCI/PCIe devices 234 connect to SB/ICH 204 through bus 238 and bus240. PCI/PCIe devices may include, for example, Ethernet adapters,add-in cards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbasic input/output system (BIOS).

HDD 226 and CD-ROM drive 230 connect to SB/ICH 204 through bus 240. HDD226 and CD-ROM drive 230 may use, for example, an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface. Super I/O (SIO) device 236 may be connected to SB/ICH 204.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within the dataprocessing system 200 in FIG. 2. As a client, the operating system maybe a commercially available operating system such as Microsoft® Windows7®. An object-oriented programming system, such as the Java™ programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java™ programs or applicationsexecuting on data processing system 200.

As a server, data processing system 200 may be, for example, an IBM®eServer™ System p® computer system, running the Advanced InteractiveExecutive (AIX®) operating system or the LINUX® operating system. Dataprocessing system 200 may be a symmetric multiprocessor (SMP) systemincluding a plurality of processors in processing unit 206.Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as HDD 226, and may be loaded into main memory 208 for execution byprocessing unit 206. The processes for illustrative embodiments of thepresent invention may be performed by processing unit 206 using computerusable program code, which may be located in a memory such as, forexample, main memory 208, ROM 224, or in one or more peripheral devices226 and 230, for example.

A bus system, such as bus 238 or bus 240 as shown in FIG. 2, may becomprised of one or more buses. Of course, the bus system may beimplemented using any type of communication fabric or architecture thatprovides for a transfer of data between different components or devicesattached to the fabric or architecture. A communication unit, such asmodem 222 or network adapter 212 of FIG. 2, may include one or moredevices used to transmit and receive data. A memory may be, for example,main memory 208, ROM 224, or a cache such as found in NB/MCH 202 in FIG.2.

Those of ordinary skill in the art will appreciate that the hardwaredepicted in FIGS. 1 and 2 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS. 1and 2. Also, the processes of the illustrative embodiments may beapplied to a multiprocessor data processing system, other than the SMPsystem mentioned previously, without departing from the spirit and scopeof the present invention.

Moreover, the data processing system 200 may take the form of any of anumber of different data processing systems including client computingdevices, server computing devices, a tablet computer, laptop computer,telephone or other communication device, a personal digital assistant(PDA), or the like. In some illustrative examples, data processingsystem 200 may be a portable computing device that is configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data, for example. Essentially, dataprocessing system 200 may be any known or later developed dataprocessing system without architectural limitation.

FIG. 3 illustrates a QA system pipeline for processing an input questionin accordance with one illustrative embodiment. The QA system pipelineof FIG. 3 may be implemented, for example, as QA system pipeline 108 ofQA system 100 in FIG. 1. It should be appreciated that the stages of theQA system pipeline shown in FIG. 3 may be implemented as one or moresoftware engines, components, or the like, which are configured withlogic for implementing the functionality attributed to the particularstage. Each stage may be implemented using one or more of such softwareengines, components or the like. The software engines, components, etc.may be executed on one or more processors of one or more data processingsystems or devices and may utilize or operate on data stored in one ormore data storage devices, memories, or the like, on one or more of thedata processing systems. The QA system pipeline of FIG. 3 may beimplemented, for example, in one or more of the stages to implement theimproved mechanism of the illustrative embodiments described hereafter.

As shown in FIG. 3, the QA system pipeline 300 comprises a plurality ofstages 310-380 through which the QA system operates to analyze an inputquestion and generate a final response. In an initial question inputstage 310, the QA system receives an input question that is presented ina natural language format. That is, a user may input, via a userinterface, an input question for which the user wishes to obtain ananswer, e.g., “Who were Washington's closest advisors?” In response toreceiving the input question, the next stage of the QA system pipeline500, i.e. the question and topic analysis stage 320, parses the inputquestion using natural language processing (NLP) techniques to extractmajor features from the input question, classify the major featuresaccording to types, e.g., names, dates, or any of a plethora of otherdefined topics. For example, in the example question above, the term“who” may be associated with a topic for “persons” indicating that theidentity of a person is being sought, “Washington” may be identified asa proper name of a person with which the question is associated,“closest” may be identified as a word indicative of proximity orrelationship, and “advisors” may be indicative of a noun or otherlanguage topic.

The identified major features may then be used during the questiondecomposition stage 330 to decompose the question into one or morequeries that may be applied to the corpus of data/information 345 inorder to generate one or more hypotheses. The queries may be generatedin any known or later developed query language, such as the StructureQuery Language (SQL), or the like. The queries may be applied to one ormore databases storing information about the electronic texts,documents, articles, websites, and the like, that make up the corpus ofdata/information 345. That is, these various sources themselves,collections of sources, and the like, may represent different corpora347 within the corpus 345. There may be different corpora 347 definedfor different collections of documents based on various criteriadepending upon the particular implementation. For example, differentcorpora may be established for different topics, subject mattercategories, sources of information, or the like. As one example, a firstcorpora may be associated with healthcare documents while a secondcorpora may be associated with financial documents. Alternatively, onecorpora may be documents published by the U.S. Department of Energywhile another corpora may be IBM Redbooks documents. Any collection ofcontent having some similar attribute may be considered to be a corpora347 within the corpus 345.

The queries may be applied to one or more databases storing informationabout the electronic texts, documents, articles, websites, and the like,that make up the corpus of data/information, e.g., the corpus of data106 in FIG. 1. The queries being applied to the corpus ofdata/information at the hypothesis generation stage 340 to generateresults identifying potential hypotheses for answering the inputquestion which can be evaluated. That is, the application of the queriesresults in the extraction of portions of the corpus of data/informationmatching the criteria of the particular query. These portions of thecorpus may then be analyzed, such as to extract particular featureswithin the portions of the corpus, and used, during the hypothesisgeneration stage 540, to generate hypotheses for answering the inputquestion. These hypotheses are also referred to herein as “candidateanswers” for the input question. For any input question, at this stage340, there may be hundreds of hypotheses or candidate answers generatedthat may need to be evaluated.

The QA system pipeline 300, in stage 350, then performs a deep analysisand comparison of the language of the input question and the language ofeach hypothesis or “candidate answer” as well as performs evidencescoring to evaluate the likelihood that the particular hypothesis is acorrect answer for the input question. As mentioned above, this mayinvolve using a plurality of reasoning algorithms, each performing aseparate type of analysis of the language of the input question and/orcontent of the corpus that provides evidence in support of, or not, ofthe hypothesis. Each reasoning algorithm generates a score based on theanalysis it performs which indicates a measure of relevance of theindividual portions of the corpus of data/information extracted byapplication of the queries as well as a measure of the correctness ofthe corresponding hypothesis, i.e. a measure of confidence in thehypothesis.

In the synthesis stage 360, the large number of relevance scoresgenerated by the various reasoning algorithms may be synthesized intoconfidence scores for the various hypotheses. This process may involveapplying weights to the various scores, where the weights have beendetermined through training of the statistical model employed by the QAsystem and/or dynamically updated, as described hereafter. The weightedscores may be processed in accordance with a statistical model generatedthrough training of the QA system that identifies a manner by whichthese scores may be combined to generate a confidence score or measurefor the individual hypotheses or candidate answers. This confidencescore or measure summarizes the level of confidence that the QA systemhas about the evidence that the candidate answer is inferred by theinput question, i.e. that the candidate answer is the correct answer forthe input question.

The resulting confidence scores or measures are processed by a finalconfidence merging and ranking stage 370 which may compare theconfidence scores and measures, compare them against predeterminedthresholds, or perform any other analysis on the confidence scores todetermine which hypotheses/candidate answers are the most likely to bethe answer to the input question. The hypotheses/candidate answers maybe ranked according to these comparisons to generate a ranked listing ofhypotheses/candidate answers (hereafter simply referred to as “candidateanswers”). From the ranked listing of candidate answers, at stage 380, afinal answer and confidence score, or final set of candidate answers andconfidence scores, may be generated and output to the submitter of theoriginal input question.

As shown in FIG. 3, in accordance the illustrative embodiments questiondisambiguation logic 390 is provided for interfacing with the hypothesisand evidence scoring stage 350 logic and the synthesis stage 360 logicto determine whether disambiguation of the input question 310 is neededand, if so, to perform operations for identifying differentiatingfactors in the evidence passages leading to the candidate answers andinteracting with a user that submitted the input question 310 to obtainuser input for clarifying the context of the input question 310 based onthe identified differentiating factors.

As shown in FIG. 3, the question disambiguation logic 390 comprisescontext clarification logic 392, differentiating factor determinationlogic 394, and user collaboration logic 396. The context clarificationlogic 392 analyzes the set of candidate answers to determine if thereare multiple “correct” candidate answers identified from the corpus ofinformation. The determination of whether there are multiple “correct”candidate answers may be as simple as determining if a more than onecandidate answer generated by the hypothesis generation stage 340 andscored by the hypothesis and evidence scoring stage 350 has a confidencescore equal to or exceeding a predetermined threshold confidence score,indicating that the candidate answer is likely a correct answer for theinput question 310. Alternatively, a determination may be used in whicha determination is made as to whether a highest scoring candidateanswer, of a plurality of candidate answers, has a confidence scoreequal to or higher than a predetermined threshold, in which case it isreturned as the correct answer for the input question 310, and if not,then multiple “correct” candidate answers are determined to be present.

In yet another possible embodiment, the determination may be morecomplex and involve multiple determinations, comparisons with aplurality of threshold values, and the like. For example, in oneillustrative embodiment, a first determination may be made to determineif any of the candidate answers have a corresponding confidence scoreabove a first threshold indicating that the candidate answer is highlylikely to be the correct answer for the input question 310, e.g., aconfidence score of 90% confidence or higher. If more than one candidateanswer has a confidence score equal to or higher than this firstthreshold, then a determination is made that there are multiple“correct” candidate answers for the input question 310. If only onecandidate answer has a confidence score equal to or higher than thisfirst threshold, then a determination may be made that the candidateanswer having the confidence score equal to or higher than the firstthreshold is the correct answer for the input question 310 and may bereturned as the correct answer to the submitter of the input question310. Alternatively, if multiple candidate answers have confidence scoresequal to or above this first threshold, all of the candidate answershaving confidence scores equal to or above this first threshold may bereturned as correct answers for the input question 310, or a candidateanswer having the highest confidence score may be selected from amongstthese candidate answers whose confidence scores are equal to or abovethis first threshold. Thus, the first threshold defines a demarcationline where some candidate answers are determined to be correct answersfor the input question (those whose confidence scores are equal to orabove the first threshold) and other candidate answers are either notcorrect answers to the input question 310 or are potentially correctcandidate answers for the input question 310 but further clarificationof the context of the input question 310 may be required.

If none of the candidate answers are determined to have confidencescores equal to or above this first threshold, a second determinationmay be made as to whether a plurality of candidate answers haveconfidence scores above a second threshold, e.g., a confidence score of60% confidence or higher. If more than one candidate answer has aconfidence score equal to or higher than this second threshold, then itmay be determined that there are multiple “correct” candidate answersfor the input question 310 and other candidate answers having confidencescores less than the second threshold may be discarded. This secondthreshold, along with the first threshold, essentially defines a rangeof confidence scores where candidate answers are potentially correctanswers for the input question 310. If multiple candidate answers fallwithin this range of confidence scores, then further clarification ofthe context of the input question 310 may be requested to ascertainwhich, if any, of the candidate answers is a correct answer for theinput question 310. Any number of determinations, comparisons, thresholdvalues, and the like, may be used without departing from the spirit andscope of the present invention.

In response to the context clarification logic 392 determining thatthere are multiple “correct” candidate answers, the contextclarification logic 392 instructs the differentiating factordetermination logic 394 to analyze the evidence passages used togenerate each of the multiple “correct” candidate answers to identifydifferentiating factors, e.g., facts, concepts, or semanticrelationships, in the various evidence passages that differentiate one“correct” candidate answer from one or more of the other “correct”candidate answers. The differentiating factor determination logic 394may interface with the hypothesis and evidence scoring stage 350 logicto analyze the features of the evidence passages to identify thefeatures indicative of differentiating factors leading to the generationof the candidate answers. Thus, the differentiating factor determinationlogic 394 may compare similar types of features from the evidencepassages, identify features of similar type that have different content,and then identify those features as being differentiating factors.

The differentiating factors essentially identify a difference in thecontext of the evidence passages that support the candidate answers.That is, using the example previously set forth above, the following twoevidence passages may be analyzed to extract various features by thehypothesis generation stage 340 logic and scored by the hypothesis andevidence scoring stage 350 logic:

-   -   “The historian Bartolome Mitre stated that Manuel Belgrano held        a deep admiration for George Washington, leader of the American        Revolution and first President of the United States. Because of        it, he worked in the translation of George Washington's Farewell        Address into the Spanish language.”    -   “William Bullein Johnson (A.M. 1814)—South Carolina Baptist        leader and first president of the Southern Baptist Convention.        Associate of first president of Columbian College (later The        George Washington University) William Staughton and Luther        Rice.”        The extraction of features may be done using any suitable type        of natural language processing (NLP) analysis or the like. The        extraction of features may take many different forms and may        look at many different types of features within the evidence        passages including a topic of the evidence passage, parts of        speech of the portions of the evidence passages, domain specific        analysis, keyword or key phrase identification, and the like.        The extraction of features may be done specifically for the        particular input question 310 based on the identification of the        type of answer that the input question 310 is requesting. That        is, through the analysis of the input question 310 by the        question and topic analysis stage 320 logic and the question        decomposition stage 330 logic, it may be determined that the        input question 310 “Who was the first president?” is looking for        a name of a person and specifically the name of a first        president. In extracting features from the evidence passages to        identify differentiating factors, the extraction may be with        regard to features specifically directed to clarifying the        context of the name of a particular person and a particular type        of first president. In the above example, such features include        “of the United States” and “of the Southern Baptist Convention.”        Thus, the differentiating factors that are determined to exist        between the two evidence passages include the focus of the term        “president” being the “United States” and the “Southern Baptist        Convention” in the corresponding evidence passages.

There may be multiple extracted features from the evidence passages thatare evaluated by the differentiating factor determination logic 394 toidentify those extracted features that are indicative of a context ofthe candidate answer within the evidence passage and which havedifferences that may differentiate one candidate answer from another.Thus, there may be multiple types of extracted features for which theevidence passages have differing content. For example, if an extractedfeature is a focus of the term “president”, then the two examplepassages above have different focuses of the “United States” and“Southern Baptist Convention.” Other types of extracted features mayalso be identified in the evidence passages as well. Thus, with themechanisms of the illustrative embodiments, differentiating factors thatare specific to differentiating the candidate answers, and specific tothe focus of the input question 310, may be identified.

Based on the identification of the differentiating factors between theevidence passages supporting the various candidate answers, the contextclarification logic 392 may forward the differentiating factors, thefeature types, and the like, to the user collaboration logic 396 for usein obtaining user input to clarify the implied context of the inputquestion 310. That is, the context clarification logic 392 formulatesone or more user interfaces for requesting user feedback input thatfurther clarifies the implied context of the input question 310. Theuser interfaces may present one or more questions and fields or otheruser interface elements for receiving from a user input indicating aclarification of the implied context of the input question 310.

For example, based on the analysis of the focus of the input question310 and the differentiating factors of the evidence passage, one or moreclarification questions may be generated requesting clarification of thefocus of the input question 310 and presenting the user with a free-forminput field, list of potential answers to the one or more clarificationquestions, fields for answering yes/no to the one or more clarificationquestions, or the like. The user interfaces may be output to thecomputing device that submitted the input question 310 so that the userinterfaces are output to the user via the computing device. The user maysubmit user input to the user interfaces which may then be returned bythe computing device to the QA system pipeline 300 for use by the usercollaboration logic 396. The user input may be used to further clarifythe implied context of the input question 310 and adjust the scoring ofcandidate answers (hypothesis), eliminate candidate answers altogether,or the like.

For example, from the evidence passages in the example above, the QAsystem may form two hypotheses that “George Washington” and “WilliamJohnson” are candidate answers for the submitted question. However, thequestion cannot be answered with confidence without additional contextin the submitted question to specify the president of what. From ananalysis of the input question 310, it can be determined that the focusof the input question 310 is the president of something and the analysisof the candidate answers and evidence passages indicate that thecandidate answers are names of presidents of something and that these“somethings” are the differentiating factors between the candidateanswers, e.g., the United States and the Southern Baptist Convention.

Thus, a user interface may be generated with a clarification question tobe presented to the user requesting that the user specify the president“of what?” The user interface may include a free-form text field intowhich the user may answer the clarification question in a free-formmanner with the user's input text being analyzed and compared againstthe differentiating factors to select one of the differentiating factorsas being indicative of the implied context of the input question 310.Alternatively, the user interface may present the differentiatingfactors that may answer the clarification question in a multiple choicemanner with corresponding selection user interface elements that allow auser to select one or more of the differentiating factors as an answerto the clarification questions.

In still a further embodiment, the clarification questions may bepresented in a “yes/no” format in which the differentiating factors arepart of the clarification question. An example of such a clarificationquestion may be of the type “Do you want the first president of theUnited States? Yes/No”. The user may select a “Yes” or “No” userinterface element to respond to the clarification question. A series ofsuch questions may be presented in the user interface, or a series ofuser interfaces, until a “Yes” answer is returned by the user. In thisway, the various different implied contexts may be explored allowing theuser to specify which implied context applies to the input question 310.

The user input into the user interface(s) is returned to the QA systempipeline 300 and received by the user collaboration logic 396 whichinforms the context clarification logic 392 of the user's identificationof the correct differentiating factor indicative of the implied contextof the input question 310. The user input may be used by the contextclarification logic 392 to instruct the synthesis stage 360 logic and/orthe hypothesis and evidence scoring stage 350 logic to eliminatecandidate answers (hypothesis) from consideration, adjust weights orother factors used to generate confidence scores for the variouscandidate answers, or otherwise adjust the confidence scoring of thevarious candidate answers to favor those candidate answers correspondingto the implied context clarified by the user input and/or decrease thefavorability of candidate answers that do not correspond to the impliedcontext clarified by the user input. That is, confidence scoresassociated with candidate answers having differentiating factors whosecontent is “United States” may be more heavily weighted in response tothe user submitting user input indicating that the user intended theinput question 310 “Who was the first president?” to refer to the firstpresident of the United States. Confidence scores associated with othercandidate answers may be left the same or have their weights reducedrelatively so as to decrease their ranking within the candidate answersreturned by the QA system pipeline 300. Alternatively, candidate answersthat do not have differentiating factors whose content is “UnitedStates” may be eliminated from the list of candidate answers undergoingsynthesis in the synthesis stage 360.

As a result, the candidate answers that are considered during the finalconfidence merging and ranking stage 370 and final answer and confidencestage 380 are modified from those originally generated by the hypothesisgeneration stage 340. The modified set of candidate answers are modifiedaccording to the clarification of the implied context of the inputquestion 310 made by the user in response to the identification ofdifferentiating factors between candidate answers in evidence passagessupporting the various candidate answers. Thus, the mechanisms of theillustrative embodiments provide the ability to clarify the context ofan input question to thereby improve the answers generated by the QAsystem pipeline 300.

Many modifications and additions may be made to the embodimentsdescribed above without departing from the spirit and scope of theillustrative embodiments. In one illustrative embodiment, a matrix maybe generated with entries corresponding to the various extractedfeatures of the evidence passages that are indicative of differentiatingfactors. Counts may be associated with each entry in the matrix toidentify how many evidence passages contain the correspondingdifferentiating factor. For example, there may be an entry in the matrixfor “United States” and another entry for “Southern Baptist Convention.”If 4 different evidence passages contain the extracted feature of“United States” then a corresponding count for the entry in the matrixmay be set to a value of 4. Similarly, if 2 different evidence passagescontain the extracted feature of “Southern Baptist Convention,” then acorresponding count for the entry in the matrix may be set to a value of2.

The counts for each of the entries may be used to identify whichdifferentiating factors are most likely to clarify the input question310 such that a final clarification of the input question 310 foridentifying a final answer is reached in a fastest possible manner. Forexample, the differentiating factors of a particular type of extractedfeature that have equal to or above a predetermined threshold countnumber may be selected for use in generating the user interfaceclarification questions. Alternatively, a predetermined top number ofdifferentiating factors, e.g., the top 2 differentiating factors, of aparticular type of extracted feature may be selected for use ingenerating the user interface clarification question. Various selectioncriteria may be used based on the matrix of entries and correspondingcounts to thereby select the differentiating factors that are mostlikely to clarify the implied context of the input question 310 andresult in a rapid identification of a correct answer for the inputquestion 310.

As discussed above, based on the identification of differentiatingfactors in the evidence passages, one or more user interfaces having oneor more clarification questions may be generated and output to a uservia their computing device to thereby solicit user input to clarify theimplied context of the input question. FIGS. 4A-4C illustrate examplesof user interfaces and clarification questions that may be generated inaccordance with illustrative embodiments of the present invention. FIG.4A illustrates an example of a user interface in which a clarificationquestion is presented in a multiple-choice answer format. With thisexample, the clarification question is presented and a listing ofpotential answers to the clarification question is also presented alongwith user selectable fields for identify which of the potential answersthe user believes to be indicative of the implied context of theoriginal input question. An potential answer of “none of the above” mayalso be presented in the case that the user believes none of the otherpotential answers are indicative of the implied context. If this is thecase, a second user interface may be generated with anotherclarification question using other differentiating factors may bepresented or a user interface indicating that clarification cannot beidentified and that the input question cannot be answered with certaintymay be presented.

FIG. 4B shows another example user interface in which the clarificationquestion is presented in a format in which the user may enter the answerto the clarification question via a free-form field. Because the usermay enter any text that they believe appropriate to answer theclarification question. Further analysis of the user's input may berequired by the elements 392-396 to compare the user's input todifferentiating factors to thereby identify which evidence passagescorrespond to the user's input and which of their correspondingcandidate answers are likely the correct answer to the input questionbased on the clarified implied context of the input question.

FIG. 4C illustrates another example user interface in which theclarification question is presented in a format of a yes/no question. Inthis case, the differentiating factor, e.g., “United States,” ispresented as part of the clarification question with the answer to theinput question being either “Yes” or “No.” If the user selects the “Yes”answer, then the corresponding differentiating factor is identified asclarifying the implied context of the input question. If the “No” answeris selected, further user interface(s) and/or clarification questionswith other differentiating factors may be presented in the yes/no formatuntil a “Yes” answer is returned or there are no other differentiatingfactors available to generate clarification questions. If there are nofurther differentiating factors available to generate clarificationquestions and a “Yes” answer has not been returned, then a userinterface may be presented that indicates that the input question couldnot be answered with sufficient confidence.

FIG. 5 is a flowchart outlining an example operation for clarifying theimplied context of an input question in accordance with one illustrativeembodiment. As shown in FIG. 5, the operation starts by receiving aninput question (step 510). Candidate answers are generated from a corpusof information using a QA system pipeline (step 520). The candidateanswers are analyzed to determine if the implied context of the inputquestion needs to be clarified (step 530).

A determination if made as to whether clarification of the impliedcontext of the input question is needed (step 540). As described above,this determination may be made based on whether there are multiplepotentially “correct” candidate answers for the input question or not.If clarification is not needed, then the candidate answers are processedby the QA system pipeline without further clarification of the impliedcontext of the input question (step 545). The operation then continue tostep 600 where a final answer and/or ranked listing of candidate answersis generated and output to the submitted of the input question followedby termination of the operation.

If clarification is determined to be needed, then the differentiatingfactors between the evidence passages for candidate answers areidentified (step 550). As mentioned above, in some illustrativeembodiments, this may involve the generation of a matrix ofdifferentiating factors and corresponding counts which may be used toselect which differentiating factors are most likely to clarify theimplied context of the input question.

Based on the identification of the differentiating factors, one or moreuser interfaces having one or more clarification questions are generatedand output to a submitter of the input question (step 560). The userresponse input to the user interface(s) is then received from thesubmitter (step 570) and the candidate answers are updated based on theuser response input (step 580). As discussed above, this may involveeliminating some candidate answers from further consideration, changingweights applied to confidence scores, or components of confidencescores, or the like. Synthesis, merging/ranking, and final answerselection via the QA system pipeline are then performed based on themodified candidate answers (step 590). The final answer and/or rankedlisting of candidate answers may then be generated and output to thesubmitter of the input question (step 600). The operation thenterminates.

Thus, the illustrative embodiments provide mechanisms for clarifying theimplied context in an originally submitted input question. Theillustrative embodiments cause the QA system to generate more accurateanswers for input questions when multiple potentially correct answersare determined to be present in a set of originally generated candidateanswers. Through user collaboration, based on automatically identifieddifferentiating factors in evidence passages supporting the variouspotentially correct answers, the implied context of the originally inputquestion may be ascertained and used to select the candidate answer(s)that are most likely the correct answer for the input question andcorrespond to the implied context of the originally input question.

As noted above, it should be appreciated that the illustrativeembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In one example embodiment, the mechanisms of theillustrative embodiments are implemented in software or program code,which includes but is not limited to firmware, resident software,microcode, etc.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers. Network adapters mayalso be coupled to the system to enable the data processing system tobecome coupled to other data processing systems or remote printers orstorage devices through intervening private or public networks. Modems,cable modems and Ethernet cards are just a few of the currentlyavailable types of network adapters.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the art. Theembodiment was chosen and described in order to best explain theprinciples of the invention, the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A computer program product comprising a computerreadable storage medium having a computer readable program storedtherein, wherein the computer readable program, when executed on acomputing device, causes the computing device to: receive an inputquestion for generation of an answer to the input question; generate aset of candidate answers for the input question based on an analysis ofa corpus of information, wherein each candidate answer in the set ofcandidate answers corresponds to an evidence passage supporting thecandidate answer as answering the input question; determine, based onthe set of candidate answers, whether clarification of the inputquestion is required; identify, in response to a determination thatclarification of the input question is required, a differentiatingfactor in evidence passages of at least two candidate answers in the setof candidate answers; send, by the data processing system, in responseto a determination that clarification of the input question is required,a request for user input to clarify the input question, wherein therequest for user input is generated based on the identifieddifferentiating factor; receive user input from the computing device inresponse to the request; and select at least one candidate answer in theset of candidate answers as an answer for the input question based onthe user input, wherein identifying a differentiating factor in evidencepassages of at least two candidate answers in the set of candidateanswers comprises: identifying a plurality of differentiating factorsbetween evidence passages of the at least two candidate answers; andselecting a subset of differentiating factors from the plurality ofdifferentiating factors based on an evaluation of which differentiatingfactors in the plurality of differentiating factors clarify the inputquestion.
 2. The computer program product of claim 1, wherein therequest for user input comprises a clarification question directed tothe differentiating factor and a plurality of user selectable potentialanswers to the clarification question, each answer corresponding to aportion of a corresponding one of the evidence passages, of the at leasttwo candidate answers, directed to the differentiating factor.
 3. Thecomputer program product of claim 1, wherein the request for user inputcomprises a clarification question that comprises a potential answercorresponding to the differentiating factor in the content of theclarification question and user selectable potential answers in theaffirmative and negative for answering the clarification question. 4.The computer program product of claim 1, wherein the request for userinput comprises a clarification question that is directed to thedifferentiating factor and a free-form text entry field into which auser may input a textual answer to the clarification question.
 5. Acomputer program product comprising a computer readable storage mediumhaving a computer readable program stored therein, wherein the computerreadable program, when executed on a computing device, causes thecomputing device to: receive an input question for generation of ananswer to the input question; generate a set of candidate answers forthe input question based on an analysis of a corpus of information,wherein each candidate answer in the set of candidate answerscorresponds to an evidence passage supporting the candidate answer asanswering the input question; determine, based on the set of candidateanswers, whether clarification of the input question is required atleast by: determining whether the set of candidate answers comprisesmore than one candidate answer with a corresponding confidence scoreequal to or higher than a predetermined threshold confidence score; anddetermining that clarification of the input question is required inresponse to determining that the set of candidate answers comprises morethan one candidate answer with a corresponding confidence score equal toor higher than the predetermined threshold confidence score; send, inresponse to a determination that clarification of the input question isrequired, a request for user input to clarify the input question;receive user input from the computing device in response to the request;and select at least one candidate answer in the set of candidate answersas an answer for the input question based on the user input.
 6. Thecomputer program product of claim 1, wherein the computer readableprogram further causes the computing device to select at least onecandidate answer in the set of candidate answers as an answer for theinput question based on the user input at least by: updating the set ofcandidate answers based on the user input; and selecting the at leastone candidate answer from the updated set of candidate answers.
 7. Thecomputer program product of claim 6, wherein the computer readableprogram further causes the computing device to update the set ofcandidate answers at least by modifying confidence scores associatedwith one or more of the candidate answers in the set of candidateanswers based on the user input, wherein confidence scores for candidateanswers having evidence passages corresponding to the user input areincreased and candidate answers having evidence passages notcorresponding to the user input are decreased.
 8. The computer programproduct of claim 6, wherein the computer readable program further causesthe computing device to update the set of candidate answers at least byremoving candidate answers, from the set of candidate answers, that haveevidence passages that do not correspond to the user input.
 9. Anapparatus comprising: a processor; and a memory coupled to theprocessor, wherein the memory comprises instructions which, whenexecuted by the processor, cause the processor to: receive an inputquestion for generation of an answer to the input question; generate aset of candidate answers for the input question based on an analysis ofa corpus of information, wherein each candidate answer in the set ofcandidate answers corresponds to an evidence passage supporting thecandidate answer as answering the input question; determine, based onthe set of candidate answers, whether clarification of the inputquestion is required; identify, in response to a determination thatclarification of the input question is required, a differentiatingfactor in evidence passages of at least two candidate answers in the setof candidate answers; send, in response to a determination thatclarification of the input question is required, a request for userinput to clarify the input question, wherein the request for user inputis generated based on the identified differentiating factor; receiveuser input from the computing device in response to the request; andselect at least one candidate answer in the set of candidate answers asan answer for the input question based on the user input.
 10. Theapparatus of claim 9, wherein the request for user input comprises aclarification question directed to the differentiating factor and aplurality of user selectable potential answers to the clarificationquestion, each answer corresponding to a portion of a corresponding oneof the evidence passages, of the at least two candidate answers,directed to the differentiating factor.
 11. The apparatus of claim 9,wherein the request for user input comprises a clarification questionthat comprises a potential answer corresponding to the differentiatingfactor in the content of the clarification question and user selectablepotential answers in the affirmative and negative for answering theclarification question.
 12. The apparatus of claim 9, wherein therequest for user input comprises a clarification question that isdirected to the differentiating factor and a free-form text entry fieldinto which a user may input a textual answer to the clarificationquestion.
 13. The apparatus of claim 9, wherein the instructions furthercause the processor to select at least one candidate answer in the setof candidate answers as an answer for the input question based on theuser input at least by: updating the set of candidate answers based onthe user input; and selecting the at least one candidate answer from theupdated set of candidate answers.
 14. The apparatus of claim 13, whereinthe instructions further cause the processor to update the set ofcandidate answers at least by modifying confidence scores associatedwith one or more of the candidate answers in the set of candidateanswers based on the user input, wherein confidence scores for candidateanswers having evidence passages corresponding to the user input areincreased and candidate answers having evidence passages notcorresponding to the user input are decreased.
 15. The apparatus ofclaim 13, wherein the instructions further cause the processor to updatethe set of candidate answers at least by removing candidate answers,from the set of candidate answers, that have evidence passages that donot correspond to the user input.
 16. The computer program product ofclaim 1, wherein the differentiating factor is a factor thatdifferentiates a context of one candidate answer from another candidateanswer in the at least two candidate answers.
 17. The computer programproduct of claim 1, further comprising calculating a frequency ofoccurrence of each of the differentiating factors in the plurality ofdifferentiating factors in the evidence passages of the at least twocandidate answers, wherein selecting the subset of differentiatingfactors comprises selecting the subset of differentiating factors basedon the frequencies of occurrence associated with each of thedifferentiating factors.
 18. The computer program product of claim 17,wherein selecting the subset of differentiating factors based on thefrequencies of occurrence associated with each of the differentiatingfactors comprises selecting differentiating factors from the pluralityof differentiating factors, for inclusion in the subset ofdifferentiating factors, that have associated frequencies of occurrenceequal to or above a threshold value.